SwitchTab: Switched Autoencoders Are Effective Tabular Learners
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913185329577984 |
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| author | Wu, Jing Chen, Suiyao Zhao, Qi Sergazinov, Renat Li, Chen Liu, Shengjie Zhao, Chongchao Xie, Tianpei Guo, Hanqing Ji, Cheng Cociorva, Daniel Brunzel, Hakan |
| author_facet | Wu, Jing Chen, Suiyao Zhao, Qi Sergazinov, Renat Li, Chen Liu, Shengjie Zhao, Chongchao Xie, Tianpei Guo, Hanqing Ji, Cheng Cociorva, Daniel Brunzel, Hakan |
| contents | Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies. However, applying these methods to tabular data is challenging due to the less pronounced dependencies among data samples. In this paper, we address this limitation by introducing SwitchTab, a novel self-supervised method specifically designed to capture latent dependencies in tabular data. SwitchTab leverages an asymmetric encoder-decoder framework to decouple mutual and salient features among data pairs, resulting in more representative embeddings. These embeddings, in turn, contribute to better decision boundaries and lead to improved results in downstream tasks. To validate the effectiveness of SwitchTab, we conduct extensive experiments across various domains involving tabular data. The results showcase superior performance in end-to-end prediction tasks with fine-tuning. Moreover, we demonstrate that pre-trained salient embeddings can be utilized as plug-and-play features to enhance the performance of various traditional classification methods (e.g., Logistic Regression, XGBoost, etc.). Lastly, we highlight the capability of SwitchTab to create explainable representations through visualization of decoupled mutual and salient features in the latent space. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SwitchTab: Switched Autoencoders Are Effective Tabular Learners Wu, Jing Chen, Suiyao Zhao, Qi Sergazinov, Renat Li, Chen Liu, Shengjie Zhao, Chongchao Xie, Tianpei Guo, Hanqing Ji, Cheng Cociorva, Daniel Brunzel, Hakan Machine Learning Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies. However, applying these methods to tabular data is challenging due to the less pronounced dependencies among data samples. In this paper, we address this limitation by introducing SwitchTab, a novel self-supervised method specifically designed to capture latent dependencies in tabular data. SwitchTab leverages an asymmetric encoder-decoder framework to decouple mutual and salient features among data pairs, resulting in more representative embeddings. These embeddings, in turn, contribute to better decision boundaries and lead to improved results in downstream tasks. To validate the effectiveness of SwitchTab, we conduct extensive experiments across various domains involving tabular data. The results showcase superior performance in end-to-end prediction tasks with fine-tuning. Moreover, we demonstrate that pre-trained salient embeddings can be utilized as plug-and-play features to enhance the performance of various traditional classification methods (e.g., Logistic Regression, XGBoost, etc.). Lastly, we highlight the capability of SwitchTab to create explainable representations through visualization of decoupled mutual and salient features in the latent space. |
| title | SwitchTab: Switched Autoencoders Are Effective Tabular Learners |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.02013 |